Once a set of bilingual hierarchical rules has been extracted along with associated features, the pivoting trick can be applied to infer monolingual hierarchical paraphrase pairs (or paraphrastic patterns).

the patterns are not the final output and are actually used as rules from a monolingual SCFG grammar in order to define an English-to-English translation model.


Again, we must draw a connection between this work and that of Quirk, Brockett, and Dolan (2004) (discussed in Section 3.3) because both treat paraphrasing as monolingual translation.

as outlined in the discussion of that work, Quirk, Brockett, and Dolan use a relatively simplistic translation model and decoder which leads to paraphrases with little or no lexical variety.


They conduct no formal evaluation of the coverage of their approach but show that, in a limited setting, it is higher than that for the syntactically constrained pivot-based approach.

they perform no comparisons of their coverage with the original pivot-based approach (Bannard and Callison-Burch 2005).


The small size of the corpus, when combined with this and other such constraints, precludes the use of the corpus as training data for a paraphrase generation or extraction system.

it is fairly useful as a freely available test set to evaluate paraphrase recognition methods.


An obvious reason for this disparity could be that paraphrasing is not an application in and of itself.

the existence of similar evaluations for other tasks that are not applications, such as dependency parsing (Buchholz and Marsi 2006;Nivre et al.


In fact, it may even lead to research being duplicated across communities.

more recent work-some of it discussed in this survey-on extracting phrasal paraphrases (or patterns) does include direct evaluation of the paraphrasing itself: The original phrase and its paraphrase are presented to multiple human judges, along with the contexts in which the phrase occurs in the original sentence, who are asked to determine whether the relationship between the two phrases is indeed paraphrastic (Barzilay and McKeown 2001;Barzilay and Lee 2003;Ibrahim, Katz, and Lin 2003;Pang, Knight, and Marcu 2003).


(2005) attempt to deal with this problem by using a fuzzy algorithm to cluster speakers; this assigns each speaker a distribution over conversations rather than making a hard assignment.

the algorithm still deals with speakers rather than utterances, and cannot determine which conversation any particular utterance is part of.


Mention information alone is not sufficient for disentanglement; with only name mention and time gap features, mean one-to-one is 38 and loc 3 is 69.

name mention features are critical for our model.


Mean loc 3 increases from 72% to about 78% and mean one-to-one accuracy from 41% to about 66%.

we find no improvement at all on the test data, because the classifier has very low recall, and the resulting test annotations have far too few conversations.


The majority of sentence compression approaches only look at sentences in isolation without taking into account any discourse information.


Computing lexical chains would be relatively straightforward if each word was always represented by a single sense.

due to the high level of polysemy inherent in WordNet, algorithms developed for computing lexical chains must adopt some strategy for disambiguating word senses.


That work clarifies the definitions of MCTAG variants and the relationship between them rather than presenting new complexity results.

it suggests the possibility of proving results such as ours in its assertion that, after a standard TAG parse, a check of whether particular trees belong to the same tree set cannot be performed in polynomial time.


This leaves open the possibility of the existence of an algorithm that is polynomial in the grammar size but has an additional exponential term in the time complexity expression.

such an algorithm, if it exists, cannot be generated by application of the GHR optimization to the baseline parser.


This discussion on the optimality of the factorization algorithm crucially assumes strong equivalence with the source TL-MCTAG G. Of course there might be TL-MCTAGs that are weakly equivalent to G, that is, they generate the same language, and have rank strictly smaller than the rank of G .

finding such structurally different grammars is a task that seems to require techniques quite different from the factorization techniques we have developed in this section.


Currently, the definition of fragment does not allow splitting apart a subset of the children of a given node from the remaining ones.

if we allow binarization of the elementary trees of the source grammar, then we might be able to isolate sets of links that could not be factorized in the source grammar itself.


As presented, the factorization algorithm is designed to handle grammars in which multiple adjunction is permitted.

if multiple adjunction is disallowed and the grammar contains trees in which multiple links impinge on the same node, the use of one link at a node will disqualify any other impinging links from use.


For instance, there must be a maximal node dominating (possibly reflexively) node n 3 but not node n 4 .

this node dominates a single link, and will not be processed by the algorithm because of the requirement at line 12 in Figure 18.


the ranges over all one-to-one alignments and computing it is #P-complete (Liang, Taskar, and Klein 2006).


We typically apply this procedure in the tropical semiring (Viterbi likelihoods), so that only the best rule derivation that generated each translation candidate is taken into account when extracting feature contributions for MERT.

given the alignment transducer L, this could also be performed in the log semiring (marginal likelihoods), taking into account the feature contributions from all rule derivations, for each translation candidate.


To see the effect of this constraint, consider the following example with a source sentence s 1 s 2 and a shallow-1 grammar defined by these four rules: There are two derivations R 1 R 2 and R 1 R 3 R 4 which yield identical translations.

r 2 would not be allowed under the constraint introduced here because it does not rewrite an X 1 to an X 0 .


For mt02-05-tune, we find that in 18.5% of the sentences HiFST finds a hypothesis with lower cost than HCP.

hCP never finds any hypothesis with lower cost for any sentence.


This makes it easier for our analysis to compare the system order with the reference order.

there are 22% cases where two different orders are provided, which shows the flexibility of translation.


Inside or outside interruptions have to be allowed to obtain fluent translations for these constituents.

the allowance of interruptions is sometimes beyond the representability of BTG rules.


Although the use of snippets instead of the full documents makes our approach efficient, it introduces noise because text fragments are used instead of full sentences.

we show that state-of-theart statistical machine translation (SMT) technology is in fact robust and flexible enough to capture the peculiarities of the language pair of user queries and result snippets.


One parse was equivalent to Give [me] any people-done chemical analyses on this rock.

if that was the correct parse, then there should have been a very salient prosodic signature on the phrase people-done, and the main verb give should have been stressed.


In the simplest way, the semantic interpretation is simply executed as a program to compute an answer to a question.

in a more general case, the system can take the interpretation as an object to be reasoned about and possibly modified.


LUNAR answered 78% of the queries asked of it at the Second Annual Lunar Science Conference, and 90% of those queries fell within its scope.

lUNAR was far from being a complete solution.


The acoustic scores of the two hypotheses were virtually identical, and the correct choice happened to come second.

the system could easily have resolved the choice by using a semantic interpretation to check the trip database to learn that Bill Woods was scheduled to go to Washington, while Alan Bell was not.


Because a, c, e, f, and h all point to Tony, we say that they are coreferent.

in paraphrasing, we do not need to build a discourse entity to state that g and i are paraphrase pairs; we restrict ourselves to semantic content and this is why we check for sameness of meaning between cataracts and cloudy vision alone, regardless of whether they are a referential unit in a discourse.


We cannot decide on (non-)coreference in (2,1) as we need a discourse to first assign a referent.

we can make paraphrasing judgments without taking discourse into consideration.


Meaning alone does not make it possible to state that the two pairs in Example (5b), repeated in Example (15), or the two pairs in Example (16) are paraphrases without first solving the coreference relations.

cooperative work between paraphrasing and coreference is not always possible, and it is harder if neither of the two can be detected by means of widely used strategies.


(2006) formalized this approach with tree transducers (Graehl and Knight 2004) by using context-free parse trees to represent the target side.


Actually, this is a desirable property on which we will introduce meta category operations later.

for the sake of convenience, we would like to assign a single category to each well-formed structure.


On Chinese-to-English, the tri-gram scores of the filtered model were a little bit worse.

after 5-gram rescoring, the BLEU scores became higher than the baseline, and METEOR scores were even significantly better.


DSMs succeed in tasks like synonym detection (Landauer and Dumais 1997) or concept categorization (Almuhareb and Poesio 2004) because such tasks require a measure of attributional similarity that favors concepts that share many properties, such as synonyms and co-hyponyms.

many other tasks require detecting different kinds of semantic similarity.


As in structured DSMs, we adopt word-link-word tuples as the most suitable way to capture distributional facts.

we extend and generalize this assumption, by proposing that, once they are formalized as a threeway tensor, tuples can become the backbone of a unified model for distributional semantics.


The performance of unstructured DSMs (including Win, our own implementation of this type of model) is also high, sometimes even better than that of structured DSMs.

our best DM model also achieves brilliant results in capturing selectional preferences, a task that is not directly addressable by unstructured DSMs.


We illustrate this space in the task of discriminating verbs participating in different argument alternations.

other uses of the space can also be foreseen.


Compare this with the result for the patient role: hunter is rather distant from roe, deer, and buck, and is therefore predicted to be a bad patient of shoot.

note that hunter is still more plausible as a patient of shoot than, for example, director.


Models can only reliably predict the human ratings in this data set if they can capture the difference between verb argument positions as well as between individual fillers.

because the verb-argument pairs were created by hand and with strict requirements, many of the arguments are infrequent in standard corpora (e.g., wimp, bellboy, or knight).


achieves better correlation in the SYN PRIMARY setting than the SEM PRIMARY setting, indicating that the frequency cutoff does not harm performance as much in Experiment 2 as it did in Experiment 1.


We do not repeat Experiment 2 even though it would have been technically possible to re-use the McRae and Pado data sets and predict plausibility judgments through inverse preferences.

the data sets combine each verb with both plausible and implausible nouns, but they do not combine each noun with different verbs in a balanced fashion, so a repetition of Experiment 2 with inverse preferences would not be very informative.


To model inverse preferences, it would be necessary to use the WordNet verb hierarchy.

wordNet organizes verbs in a comparatively flat, unconnected hierarchy with a high branching factor formed by the hypernymy/troponymy ("type of") relation.


We expect some groups to correspond to Pustejovsky's qualia (Pustejovsky 1995), which constitute particularly salient events for an object, namely, their creation and typical use.

we expect corpus data to yield a more complex picture of the events connected to a noun, which manifest themselves in the form of additional, more specific meaning clusters.


These exercises are reasonably chosen and solutions are technically well prepared and exhaustively explained with an admirable degree of clarity.

the more advanced and experienced reader might find the continuous pampering by way of overly fine-grained thematic exposition and visualization a bit excessive, perhaps even wearisome.


Although the preface says that the book is aimed at both engineering and humanities students, I suspect that readers without any mathematical background would have difficulty with some of the more formal sections.

the necessary background knowledge is not great, and in general the book is clearly written and understandable; it is suitable as both an introduction to this research area and a survey of current stateof-the-art techniques.


Indeed I spent exactly one short paragraph on this, where I discussed our work in Farmer, Sproat, and Witzel (2004) as a way of setting the stage for the current discussion.

rao spends a significant portion of his response discussing the Indus symbols, the case for the script theory, and the case against the non-script theory, and repeatedly refers to our 2004 paper and claimed problems with our arguments.


The correlation coefficients in Figure 1 show that the automatic metrics do well in predicting responsiveness and pyramid scoring for machine-generated summaries.

the scores for human-generated summaries far exceed the predictions, with a large gap between predicted and actual scores.


(2004) proposed an approach to finding subjective adjectives using the results of word clustering according to their distributional similarity.

they did not tackle the prediction of sentiment polarities of the found subjective adjectives.


So far, all extracted targets are individual words (such as weight, size).

because many targets are phrases (such as battery life), we need to identify them from the extracted individual words.


The override convention makes it possible to add, delete, or modify rules.

when a rule is modified, the entire rule has to be rewritten, even if the modifications are minor.


Then, the resulting graph is compacted, coalescing nodes marked by the same type as well as indistinguishable anonymous nodes.

the resulting graph does not necessarily maintain the relaxed upward closure condition, and therefore some modifications are needed.


The merge of S 10 with S 11 results in a non-BCPO.

the additional information supplied by S 12 resolves the problem, and S 10 S 11 S 12 is bounded complete.


S 9 stipulates two nodes, typed nagr and vagr, with the intention that these nodes be coalesced with the two anonymous nodes of S 1 .

the 'merge' operation defined in the previous section cannot achieve this goal, since the two anonymous nodes in S 1 have different attributes from their corresponding typed nodes in S 9 .


Then, similarly to the merge operation, pairs of nodes marked by the same type and pairs of indistinguishable anonymous nodes are coalesced.

to the merge operation, in the attachment operation two distinguishable anonymous nodes, as well as an anonymous node and a typed node, can be coalesced.


Therefore, there is no need to preserve the classification of nodes and only the underlying PSS is of interest.

because the resolution procedure uses the compactness algorithm which is defined over signature modules, we define the following algorithms over signature modules as well.


In our case, we require also that an anonymous node be mapped only to an anonymous node and that two typed nodes, mapped to each other, be marked by the same type.

the classification of nodes as internal, imported, and/or exported has no effect on the isomorphism since it is not part of the core of the information encoded by the signature module.


They created a scenario in which a lieutenant (the user) was sent to a village for an Army peacekeeping task.

on his way, he encountered an auto accident in which his platoon's vehicle crashed into a civilian vehicle, injuring a local boy.


In Section 4.2, we find that players tend to interrupt more often at the end of a card or a poker game when they are given more time.

players do not necessarily wait for more time when the picture game is less urgent.


Some players might perform an act similar to card review, in that they communicate all of the cards in his or her hand.

the version here differs as it includes the new card just picked up, which has not been communicated before.


Probably due to the simplicity of the picture game, we find that players mostly just have a smooth continuation as if the interruption did not happen.

we do find that players sometimes make two types of context restorations-utterance restatements and card reviews-and we find that players have a higher rate of performing these when returning to the ongoing task.


The previous experiment helped us determine which features are useful for recognizing task interruptions.

the experiment was based only on utterances that start with do you have, yet not all task interruptions are initiated with do you have.


Both the segmentor of this article and our segmentor of Zhang and Clark (2007) use a global linear model trained discriminatively using the perceptron.

when comparing state items in the agenda, our 2007 segmentor treated full words in the same way as partial words, scoring them using the same feature templates.


In order to find the highest-scored segmentation with the last word being characters b, ..n − 1, the last word needs to be combined with all different segmentations of characters 0..b − 1 so that the highest scored can be selected.

because the largest-range feature templates span only over two words (see Table 1), the highest scored among the segmentations of characters 0..b − 1 with the last word being characters b ..b − 1 will also give the highest score when combined with the word b..n − 1.


Just as with the single-beam decoder, the input sentence is processed incrementally.

when a character is processed, the number of previously built state items is increased from B to kB, where B is the beam-size and k is the number of characters that have been processed.


An obvious solution to this problem is not to assign a POS to a partial word until it becomes a full word.

lack of POS information for partial words makes them less competitive compared to full words in the beam, because the scores of full words are further supported by POS and POS n-gram information.


When the speed is over 2.5 thousand characters per second, the baseline system performed better than the joint single-beam and multiple-beam systems, due to the higher segmentation accuracy brought by the fixed beam segmentor.

as the SF = segmentation F-score; JF = joint segmentation and POS-tagging F-score.


Beam-search decoding is effective with a small beam-size.

the disadvantage of this model is the difficulty of incorporating whole word information into POS-tagging.


In this system, the decoding for word segmentation and POS-tagging are still performed separately, and exact inference for both is possible.

the interaction between POS and segmentation is restricted by reranking: POS information is used to improve segmentation only for the B segmentor outputs.


When the speed was above 100 sentences per second, the pure transition-based parser outperformed the combined parser with the same speed.

as the size of the beam increases, the accuracy of the combined parser increased more rapidly.


The accuracies increased when the beam increased from 1 to 4, but fluctuated when the beam increased beyond 4.

to the development tests, the accuracy reached its maximum when the beam size was 4 rather than 16.


In contrast to the development tests, the accuracy reached its maximum when the beam size was 4 rather than 16.

the general trend of increased accuracy as the speed decreases can still be observed, and the amount of increase diminishes as the speed decreases.


For constituent parsing, we initially compare partial outputs that have been built using the same number of actions.

because different output parse trees can contain a different number of unary-reduce actions, some candidate outputs will be completed earlier than others.


There are some shortcomings of this technique, which we discuss in Rieser and Lemon (2009a).

the data are useful for our purposes because our main interest here is in multimodal presentation strategies (in the presence of some input noise).


A data analysis shows that all of the wizards are more likely to show a graphic on the screen when the number of database hits is ≥ 4.

none of the wizards strictly follows that strategy.


Ai, Tetreault, and Litman (2007), for example, show that random models outperform more accurate ones if the latter fail to provide enough coverage.

user simulations used for testing should be more accurate with respect to the data in order to test under realistic conditions (e.g., Möller et al.


Cluster-based user simulations generate explorative user behavior which is similar but not identical to user behavior observed in the original data.

to the bigram model, where the likelihood of the next user act is conditioned on the previous system action, the likelihood for the cluster-based model is conditioned on a cluster of similar system states (see Equation 2).


We relate the multimodal score (a variable obtained by taking the average of four questions) 8 to the number of items presented (DB) for each modality, using curve fitting.

to linear regression, curve fitting does not assume a linear inductive bias, but it selects the most likely model (given the data points) by function interpolation.


The mean performance measures for testing with real users are shown in Table 10.

also see example dialogues in appendix D. there is no significant difference for the performance of the secondary driving task.


This seems to contradict the previous results in Table 11, which show low error rates for the WOZ data.

this is due to the fact that most of the observations in the WOZ data set are in the region where the predictions are accurate (i.e., most of the dialogues in the WOZ data are over 14 turns long, where the curves converge).


On the one hand, the greedy algorithm is far quicker computationally (O(n) vs. O(n 2 ) for the Chu-Liu-Edmonds algorithm and O(n 3 ) for Eisner's algorithm).

it may be prone to error propagation when early incorrect decisions negatively influence the parser at later stages.


As a result, the chance of error propagation is reduced significantly when parsing these sentences.

if this was the only difference between the two systems, we would expect them to have equal accuracy for shorter sentences.


This behavior can be explained using the same reasoning as above: Shorter dependency arcs are usually created first in the greedy parsing procedure of MaltParser and are less prone to error propagation.

longer dependencies are typically constructed at the later stages of the parsing algorithm and are affected more by error propagation.


Theoretically, MSTParser should not perform better or worse for arcs of any length.

due to the fact that longer dependencies are typically harder to parse, there is still a degradation in performance for MSTParser-up to 20% in the Dependency arc precision/recall relative to predicted/gold dependency length.


Adpositions do tend to have a high number of siblings on average, which could explain MSTParser's performance on that category.

adjectives on average occur the furthest away from the root, have the shortest dependency length, and the fewest siblings.


Feature-based integration is also similar to parse reranking (Collins 2000), where one parser produces a set of candidate parses and a second-stage classifier chooses the most likely one.

feature-based integration is not explicitly constrained to any parse decisions that the first-stage parser might make.


11 It is thus quite clear that both models have the capacity to learn from features generated by the other model.

it is also clear that the graph-based MST model shows a somewhat larger improvement, both on average and for all languages except Czech, German, Portuguese, and Slovene.


trust the guide features from MST for longer dependencies (those greater than length 4) and its own base features for shorter dependencies (those less than or equal to length 4).

for dependencies of length greater than 9, the performance of Malt MST begins to degrade.


If the original rich feature representation of Malt is sufficient to separate the training data, regularization may force the weights of the guided features to be small (as they are not needed at training time).

an on-line learning algorithm will recognize the guided features as strong indicators early in training and give them a high weight as a result.


One can speculate that it will result in error propagation and, as a result, a large number of parsing errors on long dependencies as well as those close to the root.

if the algorithm is run on data that contains only deterministic local decisions and complex global decisions, such a system might not suffer from error propagation.


Finding the treewidth of a graph is an NPcomplete problem (Arnborg, Corneil, and Proskurowski 1987).

given a graph of n vertices and treewidth k, a simple algorithm finds the optimal tree decomposition in time O(n k+2 ) (Arnborg, Corneil, and Proskurowski 1987), and a variety of approximation algorithms and heuristics are known for the treewidth problem (Bodlaender et al.


Our method involves finding the optimal tree decomposition of a graph, which is in general an NP-complete problem.

the relation to tree decomposition allows us to exploit existing algorithms for this problem, such as the linear time algorithm of Bodlaender (1996) for graphs of bounded treewidth.


Given friction between the moving parts, the balance will have limited sensitivity, so that for some y that is fractionally heavier than x, the difference in weight will fail to register, and similarly for a z that is fractionally heavier than y.

it may well be that there is sufficient difference between x and z for the balance to distinguish between the weight of the two.


The polarity of these words actually makes some sense in context: Sequels and movies adapted from video games or TV series do tend to be less well-received than the average movie.

these real-world facts are not the sort of knowledge a sentiment classifier ought to be learning; within the domain of movie reviews such facts are prejudicial, and in other domains (e.g., video games or TV shows) they are either irrelevant or a source of noise.


23 21 By default, SO-CAL assigns a zero to such texts, which is usually interpreted to mean that the text is neither positive nor negative.

in a task where we know a priori that all texts are either positive or negative, this can be a poor strategy, because we will get all of these empty texts wrong: When there are a significant number of empty texts, performance can be worse than guessing.


The most straightforward way to investigate this problem would be to ask one or more annotators to re-rank our dictionaries, and compute the inter-annotator agreement.

besides the difficulty and time-consuming nature of the task, any simple metric derived from such a process would provide information that was useful only in the context of the absolute values of our −5 to +5 scale.


There are aspects of the table consistent with our shift model, for instance a general decrease in pos judgments between pos SO 3 and 5 for lower neg SO.


This may be partially attributed to the fact that the strong/weak designation for this dictionary is defined in terms of whether the word strongly or weakly indicates subjectivity, not whether the term itself is strong or weak (a subtle distinction).

the results suggest that the scale is too coarse to capture the full range of semantic orientation.


Goldsmith [2010] who also overviews word segmentation).

works that explicitly aim to treat both word segmentation and morpheme segmentation in one algorithm are included.


Further, there has been the idea that ULM could contribute to various open questions in the field of first-language acquisition (see, e.g., Brent, Murthy, and Lundberg 1995;Batchelder 1997;Brent 1999;Clark 2001;Goldwater 2007).

the connection is still rather vague and even if ULM has matured, it is not clear what implications, if any, this has for child language acquisition.


This, then, is another reason for pursuing ULM: to be able to provide language technology to language communities lacking the requisite resources.

uLM, at least as understood for the purposes of this survey, requires a written language, which would still exclude a substantial majority of the world's languages (Borin 2009).


Any intuitively plausible theory of affixation should allow abundant combination of morphemes without respect to their phonological form, which predicts that high LSV/LSE/LSM values should emerge at morpheme boundaries.

there appears to be no reason why the converse should hold-high LSV/LSE/LSM values could emerge in other places of the word as well.


The group-and-abstract approaches are also characterized by the ubiquitous use of ad hoc thresholds.

there are clear advantages in that they are in principle capable of handling non-concatenative morphology and in that issues of semantics (of stems) are addressed from the beginning.


The general idea is to exploit the pairwise preferences induced from the data by training on pairs of patterns, rather than independently on each pattern.

given a weight vector α, the score for a pattern x (a candidate answer) is given by the inner product between the pattern and the weight vector: the error function depends on pairwise scores.


For example most similarity features (FG1) are correlated with BM25(W); for this reason the selection process does not choose a FG1 feature until iteration 9.

some features do not provide a useful signal at all.


Regardless of these differences, the analysis indicates that in our noisy setting the bag-of-words representation outperforms any individual structured representation.

the bottom part of the table tells a more interesting story: The second part of our analysis indicates that structured representations provide complementary information to the bag-of-words representation.


In the foreword, Wilks defines ACs as "conversationalists or confidants" that get to know their owners, assist with Internet interactions, provide company and companionship, and build their owner's biography.

when analyzing the necessary conditions for being an AC, Pulman concludes that conversation is not needed, and Boden strongly objects to ACs as confidants because of privacy concerns.


In the foreword, Wilks states that ACs are not robots but software agents (though later he mentions that they could be furry handbags).

turkle, Bryson, and many others clearly talk about robots.


Part II begins with an identification of some linguistic shortcomings of Lambek's original grammar and introduces a number of extensions.

to the broad introduction to Lambek categorial grammar given in Part I, Part II delves into more advanced material that is less well established.


CLIR approaches are in general presented together with their statistical models, whose understanding does not require more than elementary calculus and probability theory.

the book does not present algorithms or data structures to implement the models, so it might not be a sufficient resource to build an effective CLIR system.


as in any scientific endeavor involving humans, there is an unspoken ethical dimension involved in resource construction and system evaluation, and this is especially true of MTurk.


The acceptability involves comparison with the gold standards, which usually means the manually segmented results.

to existing unsupervised approaches, we want to explore the potential of completely unsupervised approaches.


Therefore, the limitation on the maximum length really makes a negative impact on ESA.

when the maximum length of input character sequences is further increased to 50 or 100, the results are not very different, as shown in Table 6.


The exponent in LRV 2.8 1.8 1.4 1.9 words (VE1) was given, the F-measure of VE was 0.77.

the F-measure was 0.57 without a given length (VE2).


This method provides a principled way to produce utterances matching the linguistic style of a specific corpus (e.g., of an individual author) without any overgeneration phase.

standard PCFG generation methods require a treebank-annotated corpus, and they cannot model context-dependent generation decisions, such as the control of sentence length or the generation of referring expressions.


The architecture for the PE method is shown in Figure 2.

with the overgenerate-score (OS) method discussed in Section 1.1, parameter estimation models predict generation decisions directly from input personality scores, in the spirit of the approach of Paiva and Evans (2005).


This agreement is lower than that for rule-based utterances, which could be due to the nature of the personality cues conveyed by PERSONAGE-RB's handcrafted parameters.

this difference could also result from the use of naive judges, which we believe are less consistent in their personality judgments.


If at least one option satisfies all of the user's requirements, this option can be found efficiently with the SR strategy.

the system does not point out trade-offs among alternatives in cases where no optimal option exists.


If the database does not contain any KLM flights that also match the user's other preferences (such as preferring direct flights to connecting ones), the system can recognize this conflict and present an explicit trade-off, as in I found a KLM flight but it requires a connection in Amsterdam.


If the globally best option in node 7 was perfect (i.e., if it was exactly what the user was looking for), the option in node 7 would dominate all other options, and the rest of the tree would be pruned.

if there is an aspect of the globally best option which does not match the user's ideal, the user will have to make some kind of trade-off.


At nodes 12 and 13, both constraints arrival-time:good and price:good have to be satisfied.

they are not satisfied and therefore these two nodes are pruned.


Arguably, mentioning too many attributes of options will also lead to memory overload, which may ultimately reduce user satisfaction.

the system must provide enough information to fully account for what constitutes the trade-off, that is, it must give the reasons why an option is potentially relevant.


Therefore, they are described as flights with availability in business class.

the justification for the indirect business class flights is that they have an arrival time that matches the user query better.


In the questionnaire data (see Figure 15) we found a general preference for UMSR-based recommendations on all four evaluation criteria.

only differences between answers to Questions 1 and 4 ("Did the system give the information in a way that was easy to understand?


We conclude that combining a summarize and refine approach with user modeling is a very promising approach to improving the user experience in terms of achieving higher task success and increasing efficiency.

there are also other parts of the presentation that could be tailored to the user (e.g., in adaptive option clustering).


Therefore, just as items for constituency parsers encode sets of partial constituency trees, items for dependency parsers can be defined using partial dependency trees.

dependency trees cannot express the fact that a particular structure has been predicted, but not yet built; this is required for grammar-based algorithms such as those of Lombardo and Lesmo (1996) and Kahane, Nasr, and Rambow (1998).


When described for head automaton grammars (Eisner and Satta 1999), this algorithm appears to be more complex to understand and implement than the previous one, requiring four different kinds of items to keep track of the state of the automata used by the grammars.

this abstract representation of its underlying semantics reveals that this parsing strategy is, in fact, conceptually simpler for dependency parsing.


Parsing schemata cannot specify control strategies that guide deterministic parsers; schemata work at an abstraction level, defining a set of operations without procedural constraints on the order in which they are applied.

deterministic parsers can be viewed as optimizations of underlying nondeterministic algorithms, and we can represent the actions of the underlying parser as deduction steps, abstracting away from the deterministic implementation details, obtaining a potentially interesting nondeterministic dependency parser.


This deduction system can easily be converted into a parsing schema by associating adequate semantics with items.

we do not show this here for space reasons, because we would first have to explain the formalism of regular dependency grammars.


11 Undirected links: Like dependency formalisms, LG represents the structure of sentences as a set of links between words.

whereas dependency links are directed, the links used in LG are undirected: There is no distinction made between heads and dependents.


Therefore, we assume that strings are extended with this symbol.

position n + 1 corresponds to a dummy word w n+1 that must not be linkable to any other, and is used by the parser for convenience, as in the schema for Yamada and Matsumoto's dependency parser (Section 3.4).


The definition of this property of structures, called k-ill-nestedness, is more declarative than that of mildly ill-nestedness.

it is based on properties that are not local to projections or subtrees, and there is no evidence that k-ill-nested structures are parsable in polynomial time.


Actually, TSVM(ENCN) is very similar to CoTrain, and it combines the results of two classifiers in the same way.

the co-training approach can train two more effective component classifiers than those used in TSVM(ENCN).


For the three monolingual baseline methods (BaseCN1, BaseCN2, and BaseCN3), the BaseCN2 and BaseCN3 methods outperform the BaseCN1 method.

the BaseCN2 and BaseCN3 methods cannot outperform the strong cross-lingual baseline methods (e.g., TSVM(ENCN), SelfTrain(ENCN)), because the Chinese training corpus is automatically collected without human checking and thus about 10% of the reviews are mistakenly labeled.


Typical aspects that impact system performance such as domain variability, combinations of different resources, and the use of unsupervised approaches are also illustrated.

this chapter may still be considered incomplete as some architectures exploiting advanced machine learning techniques, for example, kernel methods (Moschitti 2004), are not reported.


It is not meant to be an indepth description of how to build specific NLP applications.

it highlights necessary issues to be aware of in NLP research and application building.


The difference, however, can be probably increased by extending the window to six or even more words.

the window can also be shortened and the difference in precision might diminish or even be reversed.


1 The most common is an intellectual history-how I came to make all these wonderful discoveries.

i am never completely happy with my work, as it seems a pale shadow of what i think it should have been.


The PER is always lower than or equal to the WER.

a shortcoming of the PER is the fact that it does not penalize a wrong word order.


The contribution of nouns is WER(N) = 1/12 = 8.3%, of verbs is WER(V) = 2/12 = 16.7%, and of adverbs is WER(ADV) = 2/12 = 16.7%.

to WER, the standard efficient algorithms for the calculation of PER do not give precise information about contributing words.


The same happens with the verb reordering error be.

the noun reordering error Bush is not resolved by the POS-based reorderings of verbs, whereas it is at the correct position in the hierarchical system output.


For example, Petroni and Serva (2008) use the Levenshtein distance to classify 50 Austronesian languages, and claim that their obtained language phylogeny is "similar" to the results of the comparative method.

close inspection of their classification reveals some puzzling incongruities.


Interannotator agreement is too low on fine-grained judgments.

for the coarsegrained judgments of more than or less than a day, and of approximate agreement on temporal unit, human agreement is acceptably high.


The Penn Treebank (Marcus, Santorini, and Marcinkiewicz 1993) is the standard training and evaluation corpus for many syntactic analysis tasks, ranging from POS tagging and chunking, to full parsing.

it does not annotate internal NP structure.


Collins (2003, §3.1.1, §3.2, and §3.3) also describes the addition of distance measures, subcategorization frames, and traces to the parsing model.

these are not relevant to parsing NPs, which have their own submodel, described in the following section.


Collins (2003, page 602) gives the example Yesterday the dog barked, where conditioning on the head of the NP, dog, results in incorrectly generating Yesterday as part of the NP.

if the model is conditioning on the previous modifier, the, then the correct STOP category is much more likely to be generated, as words do not often come before the in an NP.


(1994) describe the *PPA* trace used in the Penn Treebank, which is applied to these permanent predictable ambiguities, or as we have called them, indeterminates.

*PPA* is also applied to cases of general ambiguity (those described in the following paragraphs), whereas we would separate the two.


(NP (NNP Bill) (CC and) (NNP Ted) ) the following example does need the NML bracket shown: (NP (DT the) (NML (NNPS Securities) (CC and) (NNP Exchange) ) (NNP Commission) ) Otherwise, its implicit structure would be as follows: The erroneous meaning here is the Securities and the Exchange Commission, rather than the correct the Securities Commission and the Exchange Commission.


(NP (NNS cars) (, ,) (NNS trucks) (CC and) (NNS buses) ) This is true even when the conjunction is missing: the entire list may still need to be bracketed before being joined to words outside the list, as shown: Conjunctions between a neither/nor pair do not need any bracketing.


When there are postmodifiers such as Corp. or Ltd., the rest of the company needs to be separated if it is longer than one word.

the tokens preceding a final adverb should be separated: (NP (NML (NN college) (NNS radicals) ) (RB everywhere) ) Names are to be left unbracketed: (NP (NNP Brooke) (NNP t.) (NNP Mossman) ) numbers, as well as Jr., Sr., and so forth, should be separated: (NP (NML (NNP William) (NNP H.) (NNP Hudnut) ) (NNP III) ) titles that are longer than one word also need to be bracketed separately.


The idea behind the system was that the rich profiles collected for people could be used in summaries of later news in order to generate informative descriptions.

the collection of information about entities from different contexts and different points in time leads to complications in description generation; for example, past news can refer to Bill Clinton as Clinton, an Arkansas native, the democratic presidential candidate Bill Clinton, U.S. President Clinton, or former president Clinton and it is not clear which of these descriptions are appropriate to use in a summary of a novel news item.


This body of research assumes a limited domain where the semantics of attributes and their allowed values can be formalized, though semantic representations and inference mechanisms are getting increasingly sophisticated (e.g., the use of description logic: Areces, Koller, and Striegnitz 2008;Ren, van Deemter, and Pan 2010).

siddharthan and Copestake (2004) consider open-domain generation of referring expressions in a regeneration task (text simplification); they take a different approach, approximating the hand-coded domainknowledge of earlier systems with a measure of relatedness for attribute-values that is derived from WordNet synonym and antonym links.


Further, we aim to generate new references to people by identifying semantic attributes that are appropriate given the context of the summary.

the GREC challenges only require the selection of an existing referring expression from a list.


As for the hearer-old/hearer-new classification, the syntactic forms of references were a significant indicator, suggesting that the centrality of the character was signaled by journalists using specific syntactic constructs in the references.

unlike the case of familiarity classification, the frequency of mention within and across documents were also significant features.


These information status distinctions are important when generating summaries of news, as they help determine both what to say and how to say it.

using these distinctions for summarization requires inferring information from unrestricted news.


This asymmetry shows up in word-based models to a limited extent: In most models the unit of prediction is a word; predictors include n-grams of any size in principle, not just words.

in a class-based model the asymmetry between predictor and predicted is more important: There is no justification for the premise (made, for example, in the Brown model) that the classes that are optimal for predictors are also the classes that are optimal for predictees.


The α parameter on lines 8-11 and 15-16 indicates that half-and whole-context models are as valuable, or nearly so, as the KN models: The interpolation weight of half/whole-context models is either 0.4 or 0.5.

the Brown class model (line 7) receives a lower weight of 0.2, indicating that it is less valuable in the interpolation with KN.


As a result, the model's estimates are close to maximum likelihood estimates for frequent events because the maximum likelihood estimator is appropriate in these cases.

the model's estimate of the probability of a word occurring in an unattested context is closer to the estimate of the class-based model.


(2002) minimize perplexity on the entire training set.

the objective function of our clustering is similarity of halfcontexts to cluster centroids or, more precisely, minimizing the residual sum of squares of differences between half-context vectors and cluster centroids.


The focus of this article is the comparison of HC and WC classes and our investigations into the context-specific characteristics of history-length interpolation and class-based generalization.

we also want to point out that the clustering algorithm we are using is very efficient, thus removing a potential obstacle to the widespread use of class-based language models.


Again, all of these studies differ significantly from our own, in their task definition, in their methodology, and in the domain they examine.

we expect this brief summary to serve as a general frame of reference for our own classification results.


Now if we take the shorthand notation of writing the basis vector in L ∞ (A * × A * ) corresponding to a pair of strings as the pair of strings itself thenb = 1 3 (a, cd) + 1 3 (a, fd) (30) It would thus seem sensible to define multiplication of contexts so that 1 3 (a, cd) • 1 3 (ab, d) = 1 3 (a, d).

we then find showing that this definition of multiplication doesn't provide us with what we are looking for.


Then L is a probability distribution over A * , because L is positive and L 1 = 1.

â 1 is infinite, because each string x for which L(x) > 0 contributes 1/2 to the value of the norm, and there are an infinite number of such strings.


LSA performs a singular value decomposition on the matrix of words and documents which brings out hidden "latent" similarities in meaning between words, even though they may not occur together.

pLSA and LDA provide probabilistic models of corpora using Bayesian methods.


To conclude, the algorithm we suggest next is applied in our experiments on focused entailment graphs.

we believe that it is suitable for any entailment graph whose properties are similar to those of focused entailment graphs.


Thus, in A3 we observe that in future iterations the strongly connected component expands further and many more wrong edges are inserted into the graph.

in B we see that ILP-Global takes into consideration the global interaction between the four nodes and other nodes of the graph, and decides to split this strongly connected component in two, which improves the precision of ILP-Global.


We could use this term to normalize the similarity score.

this would only account for unaligned or badly aligned nodes and edges in the labeled sentence while ignoring the unlabeled partner.


In order to ensure that the latter describe a partial injective function, we enforce the following constraints: We can now write Equation (1) in terms of the variables x ij (which capture exactly the same information as the function σ): Note that Equations (1) and (5) are summations of the same terms.

4 Equation (5) is not linear in the variables x ij as it contains products of the form x ij x kl .


The FEE of the labeled sentence and the target verb of the unlabeled sentence are presumed identical.

we waive this restriction in Experiment 2, where we acquire annotations for unknown FEEs, that is, predicates for which no manual annotations are available.


Accordingly these paradigmatic cases will also be the main focus of this survey, although we shall often have occasion to discuss other types of expressions.

to do full justice to indefinite or attributive descriptions, proper names, and personal pronouns would, in our view, require a separate, additional survey.


Expression s j s m s 0 includes a reference to Mia, that is, it presents Joe's epistemic stance according to Mia, based on what the author says.

expression s j s 0 refers to Joe's perspective only according to the author.


More support is needed, however, in order to confirm this claim.

such a highly linguistically based approach has its drawbacks as well, because it suffers from limitations regarding its linguistic coverage (mainly syntactic constructions), and its incapability to deal with ambiguity in natural language.


In FactBank, markers of possibility or probability, such as could or likely, uniquely determine the corresponding tag (Saurí 2008, page 233).

the Turkers allow the bias created by these lexical items to be swayed by other factors.


Normalization: Saddam accepted a border demarcation treaty Annotations: PS+: 6, PR+: 2, CT+: 2 Another difference is that nouns appearing in a negative context were tagged as CT+ by the Turkers but as CT− or PR− in FactBank.

its equity in the net income of National Steel declined to $6.3 million from $10.9 million as a result of softer demand and lost orders following prolonged labor talks and a threatened strike.


We consider propositions to which no truth value can be attributed, given the speaker's mental state, as instances of semantic uncertainty.

uncertainty may also arise at the discourse level, when the speaker intentionally omits some information from the statement, making it vague, ambiguous, or misleading.


The latter assigns a label sequence to a sentence (a sequence of tokens) thus it naturally deals with the context of a particular word.

context information for a token is built into the feature space of the token classification approaches.


The following instance of may in FactBank was correctly marked as non-cue by the cue detector when trained on Wikipedia texts.

it was marked as a cue when trained on biological texts since in this case, there were insufficient training examples of may not being a cue: (16) "Well may we say 'God save the Queen,' for nothing will save the republic," outraged monarchist delegate David Mitchell said.


On the one hand, this is desirable because it reduces the possibility of confounding effects from prior knowledge.

it would be interesting for future work to extend Fruit Carts-style domains to more realistic object construction and placement tasks.


For example, the proposition conveying the value of a bar (Proposition 2) can be represented via the predicate "presents" (Predicate 15) and the proposition conveying the average of all bar values (Proposition 4) via the predicate "averages" (Predicate 10).

some propositions require more than one predicate.


Among the eight levels defined in that study, the levels of interest in our work are simple sentences, conjoined sentences, sentences with a relative clause modifying the object of the main verb, non-finite clauses in adjunct positions, and sentences with more than one level of embedding.

the definition of sentence types at each level is too general.


The first forest has a center-embedded relative clause and receives a score of 4 for the clause criteria: the product of the complexity of the relative clause (2) and its position (2).

the second forest has a right-branching relative clause and receives a score of 2 for the same criteria: the product of the complexity of the relative clause (2) and its position (1).


Note that in the first conjoined sentence, the time period mentioned in the first conjunct (from 1998 to 2006) subsumes the time period mentioned in the second conjunct (between 2000 and 2001).

in the second conjoined sentence, the time period mentioned in the first conjunct (between 1998 to 1999) precedes the time period mentioned in the second conjunct (between 2000 and 2001).


Bohnet, Klatt, and Wanner (2002) also has similar goals to the present research, as it is aimed at automatically classifying German adjectives.

the classification used is not purely semantic, polysemy is not taken into account, and the evidence and techniques used are more limited than the ones used here.


Thus, we had expected that polysemous adjectives form a homogeneous group of lexical items, characterized precisely by the fact that they exhibit properties from each class to a certain degree.

this expectation is not borne out in the results of the experiments.


Because the maximal depth of a lexical node in an elementary tree of G 1 is 2, we deduce that every tree generated by G 1 contains a lexical node with depth at most 2.

all lexical nodes in the tree t 1 have depth 3.


−4.37441 −6.88172 −10.6397 −0.141938 −3.65821 −8.81816 −3.60823 −4.29886 −7.64586 c. −3.57338 −6.86285 −10.9656 −0.131813 −3.8662 −8.85551 −3.42688 −4.28615 −7.82392 d. −4.61674 −6.49064 −13.0595 −0.255452 −3.73302 −5.55244 −3.60481 −3.97708 −7.85289 e. −3.85647 −6.61406 −12.5666 −0.178075 −3.92356 −5.90511 −3.46416 −4.03158 −7 The conditional probability of the word(s) party or political bureau given document history computed by 5-gram/PLSA or 5-gram/PLSA+4-SLM/PLSA is significantly boosted due to the appearance of semantic related words such as cpc and communist party in the previous sentences, this clearly shows that the composite language models (5-gram/PLSA and 5-gram/PLSA+4-SLM/PLSA) trigger long-span document-level discourse topics to influence word prediction.

there is no effect when using linear combination models (i.e., 5-gram+PLSA and 5-gram+4-SLM+PLSA).


MBOTs are desirable for natural language processing applications because they are closed under composition and can be used to represent sequences of transformations of the type performed by STSGs.

the string translations produced by MBOTs representing compositions of STSGs are strictly more powerful than the string translations produced by STSGs, which are equivalent to the translations produced by SCFGs.


It has been shown to parse exhaustively with very competitive or superior efficiency compared with other highly optimized CYK parsers (Dunlop, Bodenstab, and Roark 2011).

to the results in Roark and Hollingshead (2009), here we present results with both leftand right-binarized PCFGs induced using a Markov order-2 transform, as detailed in Section 3.3, and also present results for parsing Chinese.


The event interpretation is characterized as dynamic because it implies a change from 'not being adapted' to 'being adapted.'

in Example (8) the same nominalization is understood as a result because it denotes a specific object that is the outcome of the action of adapting a creative work into a film.


Regarding this denotative distinction, several linguistic criteria have been proposed in order to identify each of these denotations, mostly for English, although there are some proposals for Spanish (Picallo 1999), French, Greek, Russian, andPolish (Alexiadou 2001) (see Table 2 in Section 4.1).

authors differ on the argument-taking capacity of deverbal nominalizations: Some linguists maintain that only event deverbal nominalizations can take arguments (Zubizarreta 1987;Grimshaw 1990), whereas others consider that both event and result nominalizations can take arguments (Pustejovsky 1995;Picallo 1999;Alexiadou 2001).


Thus, as originally presented, Kay's algorithm is correct only for a very restricted set of unification grammars.

we draw from the larger term set T F that includes in addition the collection of path-terms that combine constants with sequences of attributes.


2008); these clearly help in expressing linguistic generalizations but can be formally treated in the obvious way by translating their occurrences into the more basic descriptions that they abbreviate.

the restriction operator (Kaplan and Wedekind 1993) requires more careful consideration.


We can thus establish the context-free result for a broader family of formalisms that share the property of being endowed with a context-free base.

it is not clear whether the string set corresponding to an underlying Head-driven Phrase Structure Grammar (HPSG) feature structure is context-free.


In Hungarian, the F-scores for dative objects improve by over 33 percentage points to 73.49% F-score when switching to the PRED-M model.

although all the scores improve for German, improvements are generally low when switching from the NO-M to the PRED-M model.


2012), a German Treebank, non-local dependencies are expressed via an annotation of topological fields (Höhle 1986) and special edge labels.

some other treebanks, among them NeGra and TIGER, give up the annotation backbone based on CFG and allow annotation with crossing branches (Skut et al.


In many cases the correct segmentation cannot be determined from local context alone, but can be disambiguated by more global syntactic constraints (in ים שמ ראיתי חולים ,כ the middle token is ambiguous between ים שמ ['sky'] and ים ש-מ ['that/ rel water'], and the sequence can be interpreted as either 'I saw blue skies' or 'I saw that blue water.'

הבאר ן מ ו רצ פ חולים כ ים שמ ראיתי is unambiguous because the past verb ו רצ פ requires the relativizer ,ש allowing only the segmented ים ש-מ reading 'I saw that blue water broke from the well'.


The treebank estimates are based on a small vocabulary, the external lexicon estimates are based on a very large vocabulary, and a proper combination of the two emission probabilities is not trivial.

the tagging probabilities do not depend on the vocabulary size, allowing a very simple combination.


Looking just at nominals, we see in the gold corpus that 62% of the dependents in a modification relation have no inherent rationality (this is the case notably for adjectives), whereas this number for idafa is only 18%.

the dependent of an idafa is irrational 66% of the time, whereas for modification that number is only 16%.


This is a result of their high relevance and their high prediction accuracy.

cASE and STATE are the best performers in the gold condition (i.e., highly relevant) but not in the predicted condition (where cASE is actually the worst feature).


In contrast, English NE boundaries are easier to identify with explicit words and capitalization clues.

classification of English NE type is considered more challenging (Ji and Grishman 2006).


The reason for this is that News Agency is better aligned to "通信社", rather than be deleted, which would occur if "北韩中央" is chosen as the corresponding Chinese NE.

type consistency constraints can help correct the NE type that is less reliably identified.


Further inspection of those seven cases (which prefer the sub-string) reveals that three of them are with unmatched components (Category IV); therefore only four of them are, in fact, due to the problem of F7.

among the nine errors caused mainly by F10, only three of them chose the sub-strings of their corresponding ENE reference, and the remaining six errors selected the strings unrelated to the reference due to spurious anchors and an acronym.


We find that the best summary indeed conveys some of the main issues also reported in the human summaries.

the low-scoring summary presents a story line about one of the lawyers involved in the case, which is a peripheral topic described in only one of the input documents.


Such a structure of the abstract clusters can be explained by the fact that relationships, marriages, collaborations, and political systems are all cognitively mapped to the same source domain of MECHANISM.

to concrete concepts, such as tea, water, coffee, beer, drink, liquid, that are clustered together when they have similar meanings, abstract concepts tend to be clustered together if they are associated with the same source domain.


This is due to the fact that it does not reach beyond the concepts present in the seed set.

most metaphors tagged by the clustering method (87%) are non-synonymous to those in the seed set and some of them are novel.


Notably, z is unobserved, and we instead observe only the answer y, which is obtained by evaluating z on a world/database w. There are an exponential number of possible trees z, and usually dynamic programming can be used to efficiently search over trees.

in our learning setting (independent of the semantic formalism), we must enforce the global constraint that z produces y.


Observe that ascent and descent are both listed in the same category 694 (SLOPE), which makes sense here because both words are pertinent to the concept of slope.

two separate clues independently inform our system that the words are opposites of each other: (1) Category 49 has the word upwardness in the same paragraph as ascent, and category 50 has the word downwardness in the same paragraph as descent.


This is to be expected, as most core arguments fall under the Arg0 and Arg1 classes, which can typically be disambiguated based on syntactic information (i.e., subject vs. object).

there are no syntactic hints for adjunct arguments, so the system learns to rely more on SP information in this case.


[...] In the experiments, we work only with the terms present in WordNet [...] The evaluation is based only on the WordNet relations.

the harvesting algorithm extracts much more.


This means that if the learned taxonomy is less structured we replicate the cut k l − 1 for k r − k l times (where k l is the maximum depth of the learned taxonomy), whereas if it is more structured we stop at cut k r − 1.

to previous evaluation models, our aim is to reward (instead of penalize) more structured taxonomies provided they still match the gold standard one.


Therefore, there is no evidence of the precision of their method on new domains, where the category nodes are unknown.

if Hearst's patterns, which are at the basis of K&H's hypernymy harvesting algorithm, could show adequate precision, we would use them in combination with our definitional patterns.


Moreover, these resources do not yet tackle the dynamic evolution of language.

our WSI approach to search result clustering automatically discovers both lexicographic and encyclopedic senses of a query (including new ones), thus taking into account all of the mentioned issues.


In this approach, however, topics (estimated from a universal data set) are query-independent and thus their number needs to be established beforehand.

we aim to cluster snippets on the basis of a dynamic and finergrained notion of sense.


On the one hand, it may terminate in a non-terminal configuration where no transition can be applied.

it may fail to terminate at all, because the system allows an infinite sequence of transitions.


In this case, the system is precise but the recall is low, given that many relations are not detected.

a low value for balance causes many lowprecision constraints to have a positive weight, which increases recall but also decreases precision (see Figure 18).


The crucial difference is that when making this choice in a traditional historybased model, the model designer inevitably makes strong independence assumptions because features that are not included are deemed totally irrelevant.

iSBNs can avoid such a priori independence assumptions because information can be passed repeatedly from latent variables to latent variables along the edges of the graphical model.


It is also the only merging method we are aware of that addresses coordinated compounds.

it requires a factored decoder that can carry part-of-speech tags through the translation process.


The rate of novel compounds is relatively low in the data sets used in our experiments, mainly due to the fact that most experiments are carried out with in-domain test data.

the best methods tend to vary between the domain-restricted automotive corpora and Europarl.


The difference between a baseline using a POS-model but no compound splitting and the split-merge EPOS-models is small in all experiments and does not seem to increase with the size of the training corpus.

only the latter can produce novel compounds.


We compare our models with J&N'07 using the benchmark data set from SemEval 2007.

because we are not aware of any other work using the FrameNet 1.5 full text annotations, we report our results on that data set without comparison to any other system.


First, they identified locative, temporal, and directional prepositions using a dependency parser so as to retain them as valid LUs.

we pruned all types of prepositions because we found them to hurt our performance on the development set due to errors in syntactic parsing.


26 For automatically identified targets, the F 1 score falls because the model fails to predict frames for unseen lemmas.

our model outperforms J&N'07 by 4 F 1 points.


The precision and recall measures are significant as well (p < 0.05 and p < 0.01, respectively).

because targets identified by J&N'07 and frames classified by our frame identification model resulted in scores on par with the baseline, we note that the significant results follow due to better target identification.


Thus, the initial value on Figure 4 corresponds to a damping factor of 0.001.

a damping factor of 1 yields to the same results as the STATIC method (c.f.


The matrix also shows that all our three methods agree more than 80% of the time, with PPR and STATIC having a relatively smaller agreement.

related work using the same techniques over domain-specific words (Agirre, López de Lacalle, and Soroa 2009) shows that the results of our Personalized PageRank models departs significantly from MFS and STATIC.


Their technique selects feature subsets that minimize the distance between training text and unlabeled test text, but unlike our techniques, theirs cannot learn representations with features that do not appear in the original feature set.

we learn hidden features through statistical language models.


McClosky, Charniak, and Johnson (2010) use classifiers from multiple source domains and features that describe how much a target document diverges from each source domain to determine an optimal weighting of the source-domain classifiers for parsing the target text.

it is unclear if this "source-combination" technique works well on domains that are not mixtures of the various source domains.


As M, N → ∞, five out of every six edges from the complete lattice appear in the PL-MRF.

the PL-MRF makes the branches conditionally independent from one another, except through the trunk.


Our inference algorithm passes information from the branches inwards to the trunk, and then upward along the trunk, in time O(K 4 MN).

a fully connected lattice model has tree-width = min(M, N), making inference and learning intractable (Sutton, McCallum, and Rohanimanesh 2007), partly because of the difficulty in enumerating and summing over the exponentially-many configurations y for a given x.


The WEB1T-n-GRAM-R uses none of the local context to decide which features to provide, and the NB-R uses only the immediate left and right context, so both models ignore most of the context.

the remaining graphical models use Viterbi decoding to take into account all tokens in the surrounding sentence, which helps to explain their relative improvement over WEB1T-n-GRAM-R on polysemous words.


There is a logical underpinning or at least a formal conceptual scheme, in which the semantics of sentences can be represented.

the mapping of sentences to their formal representations is itself not defined in a fully formal way, but requires external background knowledge, heuristics, or user feedback.


There are a number of other important features that could be considered, for example, support for existential quantification, equality, and types of supported speech acts (such as declarative, interrogative, directive, and indirect speech acts).

to achieve a simple classification into a sequence of five classes, these features will turn out to be sufficient and lead to a classification that seems consistent with the intuitive understanding of expressiveness.


Concerning the last three properties, the data show similar language counts for academic and industrial CNLs: 50 and 43 languages, respectively.

only ten CNLs were found that originated from a governmental environment.


I would like to conclude with the observation that the study of controlled languages is a very dynamic and highly interdisciplinary field, for the most part occupying small niches in the academic, industrial, and governmental worlds.

adding all these niches together gives us a large body of past and ongoing work.


Those dialects, in turn, differ quite a bit from each other.

due to MSA's prevalence in written form, almost all Arabic data sets have predominantly MSA content.


Harvesting data from such sources is a viable option for computational linguists to create large data sets to be used in statistical learning setups.

because all Arabic varieties use the same character set, and furthermore much of the vocabulary is shared among different varieties, it is not a trivial matter to distinguish and separate the dialects from each other.


For example, the typical Arabic speaker has little trouble understanding the Egyptian dialect, thanks in no small part to Egypt's history in movie-making and television show production, and their popularity across the Arab world.

the Moroccan dialect, especially in its spoken form, is quite difficult to understand by a Levantine speaker.


Bayesian methods have been applied to a number of segmentation tasks in natural language processing, including word segmentation, TSG learning, and learning machine translation rules, as a way of controlling the overfitting produced when Expectation Maximization would tend to prefer longer segments.

it is important to note that the Bayesian priors in most cases control the size and number of the clusters, but do not explicitly control the size of rules.


If the stack contains exactly one word, we terminate and output a tree, which was true also in the old system.

if the stack contains more than one word, we now go on parsing but are forbidden to make any Shift transitions.


The following parameters are the priors of the Dirichlet and beta distributions used by the models.

to the number of topics, which controls model complexity, the priors allow users of the models to specify their prior knowledge and beliefs about the data.


As for the topic distribution priors, symmetric priors are often used, with a default value of 0.01 for all the vector elements (yielding sparse word distributions, as indicated earlier), meaning that each topic is expected to assign high probabilities to only a few top words (Steyvers and Griffiths 2007).

to the topic distribution priors, Wallach, Mimno, and McCallum (2009) found in their experiments on LDA that using an asymmetric β β β (D) was of no benefit.


For the same reason, using a symmetric β β β (A) is a sensible choice for AT.

to LDA and AT, our DADT model distinguishes between document words and author words, and thus uses both β β β (D) and β β β (A) as priors.


Similarly to LDA, we then setθ θ θ (A) to its expected value according to Equation (8 a .

aT is limited because all the documents by the same authors are generated in an identical manner (Section 3.3.1).


Of the LDA and AT variants presented in Sections 3.2.3 and 3.3.3, DADT might seem most similar to AT-FA.

there are several key differences between DADT and AT-FA.


This allows us to roughly gauge how much information is lost by converting texts from token representations to topic representations.

this approach ignores the probabilistic nature of the underlying topic model, and thus does not fully test the utility of the author representations yielded by the model-these are better tested by the next approach.


However, this approach ignores the probabilistic nature of the underlying topic model, and thus does not fully test the utility of the author representations yielded by the model-these are better tested by the next approach.

to dimensionality reduction methods, probabilistic methods utilize the underlying model's definitions directly to estimate the probability that a given author wrote a given test text.


In addition, we found that following an approach whereπ andθ θ θ (D) are sampled separately for each author (similarly to AT-FA-P2) yields comparable performance to sampling only once by following the previously-unknown author assumption.

the former approach is too computationally expensive to run on data sets with many candidate authors.


IMDb1M can be seen as complementary to the IMDb62 data set, as IMDb62 allows us to test scenarios in which the user population is made up of prolific users, whereas IMDb1M contains a more varied sample of the population.

because we did not impose a minimum threshold on the number of reviews or posts, the IMDb1M population is very challenging as it includes many users with few texts (e.g., about 56% of the users in IMDb1M wrote only one text).


It is likely that performing an exhaustive grid search for the optimal parameter settings for each method would allow us to obtain somewhat improved results.

such a search would be computationally expensive, as the model needs to be retrained and tested for each fold, parameter set, and method.


Specifically, when we used uninformed uniform priors on the document/author word split (δ (D) = δ (A) = 1), and the same wordin-topic priors for both document and author words ( = 0), the obtained accuracy was comparable to AT-P's accuracy.

setting δ (D) = 1.222 and δ (A) = 4.889, which encodes our prior belief that on average 80% (with a standard deviation of 15%) of each document is composed of author words, significantly improved performance.


DADT-P's testing result is comparable to the third-best accuracy (out of 17) obtained in the PAN'11 competition (Argamon and Juola 2011) (competitors were ranked according to macro-averaged and micro-averaged precision, recall, and F1; the micro-averaged measures are all equivalent to the accuracy measure in this case, because each of the test texts is assigned to a single candidate author).

to the best of our knowledge, DADT-P obtained the best accuracy for a fully supervised method that uses only unigram features.


The reason why DADT-P's performance dropped when only stopwords were used may be that DADT was designed under the assumption that all the tokens in the corpus are retained.

we are encouraged by the fact that DADT-P's performance drop on Judgment was not very large when only stopwords were retained, as it indicates that DADT captures stylistic elements in the authors' texts.


As in our previous experiments, DADT-P consistently outperformed AT-P, which indicates that using disjoint sets of document and author topics yields author representations that are more suitable for authorship attribution than using only author topics.

to the previous experiments, Token SVM outperformed DADT-P in one case: the IMDb62 data set.


DADT's improved performance in comparison with methods based on LDA and AT comes at a price of more parameters to tune.

the most important parameter is the number of topics-we found that the prior values that yielded good results on the small data sets also obtained good performance on the large data sets without further tuning.


One approach is to use tools that obfuscate author identity, as developed by, for example, Kacmarcik and Gamon (2006) and Brennan and Greenstadt (2009).

as this may lead to an "arms race" between such tools and authorship analysis methods, perhaps the best approach is to forgo anonymity completely, as advocated by some researchers and editors (Groves 2010).


Recently, rating prediction algorithms that are based on matrix factorization have become increasingly popular, due to their high accuracy and scalability (Koren, Bell, and Volinsky 2009).

such algorithms often deliver inaccurate rating predictions for users with few ratings (this is known as the new user problem).


For example, event mentions are typically predications that require more complex lexico-semantic processing, and furthermore, the capability of extracting features that characterize them has been available only since semantic parsers based on PropBank (Palmer, Gildea, and Kingsbury 2005) and FrameNet (Baker, Fillmore, and Lowe 1998) corpora have been developed.

entity coreference resolution has been intensively studied and many successful techniques for identifying mention clusters have been developed (Cardie and Wagstaf 1999;Haghighi and Klein 2009;Stoyanov et al.


The only requirement for them to infer coreference clusters of event mentions is to have the observable objects (i.e., the event mentions) identified in the order they occur in the documents as well as to have all the linguistic features associated with these objects extracted.

in order to see how well these models perform, we need to compare their results with manually annotated clusters of event mentions.


For the model depicted in Figure 3(d), for instance, the posterior probability is given by: In this model, P(FR i,j | HL i,j , θ) is a global distribution parameterized by θ, and FT is a feature type variable from the set X = HL, POS, FR .

one limitation of this particular model is that it requires domain knowledge in order to establish the dependencies between the feature type variables.


In this way, the new nonparametric extension will have the benefits of capturing the uncertainty regarding the number of mixture components that are characterized by a potentially infinite number of feature values.

to make this hybrid work, we have to devise a mechanism in which only a finite set of relevant feature values will be selected to explain each observation (i.e., event mention) in the HDP inference process.


9 The results confirm the fact that the sampling scheme of the feature values used in the iFHMM-iHMM framework does not guarantee the selection of the most salient features.

the constant trend in the performance values shown in Figure 9 proves that iFHMM-iHMM is a robust generative model for handling noisy and redundant features.


Moreover, we believe that the HDP extension can be used for solving clustering problems that involve a small number of feature types and a priori known facts about the salience of these feature types.

when no such prior information is known with respect to the number of feature types, or the total number of features is relatively large, we believe that the iFHMM-iHMM model is a more suitable choice.


One way to address this is to also extract rules that use part-of-speech (POS) tags in place of words.

since words can have multiple POS tags, we would then need to infer POS tags for the words in order to determine which rule is applicable.


For example, for a three-word source sentence, there cannot exist a directed path from a node with coverage vector 0, 1, 0 to a node with coverage vector 0, 0, 1 .

there may or may not be a path from a node with vector 0, 1, 0 to one with 0, 1, 1 .


For example, we can paraphrase a high percentage of by a large number of in the sentence a form of asbestos has caused a high percentage of cancer deaths.

text paraphrasing may have more effect on the grammaticality of a sentence than lexical substitution.


Note that the receiver does not need to know the original cover text.

not all the linguistic transformations can meet this requirement.


In the SemEval-2007 lexical substitution task participants were asked to discover possible replacements of a target word so the evaluation metrics provided are designed to give credit for each correct guess and do not take the ordering of the guesses into account.

in the ranking task a system is already given a fixed pool of substitutes and is asked to recover the order of the list.


That is, from a security perspective, rejecting an acceptable substitute does not damage the quality of stego text.

it will lower the payload capacity so more stego text transmission is needed in order to send the secret message, which may raise a security concern.


Note that it is acceptable to have conjoin and join encoded by the same codeword 00 because both of them have access to all the two-bit codewords.

both bind and draw have only one neighbor, which means that only two codewords can be accommodated by these nodes, namely, bits 0 and 1.


The combined classifier resulted in a much higher accuracy than any of the two methods alone.

the use of BN is not central to this work, and its structure does not reflect any insights or intuitions on the structure of the problem domain or on interdependencies among features.


The main advantage of the rule-based NER systems is that they are based on a core of solid linguistic knowledge (Shaalan 2010).

any maintenance or updates required for these systems is labor-intensive and time-consuming; the problem is compounded if the linguists with the required knowledge and background are not available.


Arabic is at a disadvantage in this regard because the script does not orthographically mark proper names in this way.

many researchers (e.g., Benajiba, Diab, and Rosso 2008a;Mohit et al.


YASMET is designed to handle a large set of features efficiently.

there are not many details available about the features of this toolkit.


This classifier uses 19 distinct and weighted morphological features to provide complete diacritic, lexemic, glossary, and morphological information (Habash 2010).

because MADA is built on top of BAMA, it inherits all of BAMA's limitations.


As we showed earlier, WordNet (Miller 1995) has been used for this task.

even though W have been built for other languages, their coverage is relatively limited when compared to the English WordNet.


For large-scale data sets, on-line training methods can be much faster than batch training methods.

we find that the existing on-line training methods are still not good enough for training large-scale NLP systems-probably because those methods are not well-tailored for NLP systems that have massive features.


(2011) modify the Web query approach to better capture statistical association by using pointwise mutual information (PMI) rather than raw co-occurrence frequency to quantify selectional preference: The role of the PMI transformation is to correct for the effect of unigram frequency: A common word may co-occur often with another word just because it is a common word rather than because there is a semantic association between them.

it does not provide a way to overcome the problem of inaccurate counts for low-probability cooccurrences.


We have attempted to identify general factors that predict the difficulty of an item by measuring rank correlation between the per-item pseudo-coefficients and various corpus statistics.

it has proven difficult to isolate reliable patterns.


Our role labeler is fully unsupervised with respect to both tasks-it does not rely on any role annotated data or semantic resources.

our system does not learn from raw text.


2009), which was also derived from the SALSA corpus.

we did not convert the original constituent-based SALSA representation into dependencies, as we wanted to assess whether our methods are also compatible with phrase structure trees.


This also explains why there is little variation in the collocation and purity results across methods.

qualitatively the tradeoff between purity and collocation is the same as for English (i.e., purity is increased at the cost of collocation).


Though we cannot compute the true bias for any real system, the computation is trivial for this baseline.

the true V-measure is equal to 0, as the baseline can be regarded as a limiting case of a stochastic system that picks up one of the m clusters under the uniform distribution with m → ∞; the mutual information between any class labels and clustering produced by such model equals 0 for every m. the ML estimate for the V-measure isV(k, c) = 2Ĥ(c)/(log N +Ĥ(c)).


For this reason, we do not use PDAs to implement Step 1 in decoding: throughout this article a CYK-like parsing algorithm is always used for Step 1.

we do use PDAs to represent the regular languages produced in Step 1 and in the intersection and shortest distance operations needed for Steps 2 and 3.


In this section, in order to simplify the presentation we will only consider machines over the tropical semiring (R + ∪ {∞}, min, +, ∞, 0).

for each operation, we will specify in which semirings it can be applied.


We first describe the unpruned expansion.

in practice a pruning strategy of some sort is required to avoid state explosion.


The complexity of the algorithm is linear in the size of T ′ .

the size of T ′ can be exponential in the size of T, which motivates the development of pruned expansion, as discussed next.


in M, because there is a transition leaving state s i labeled with w, the backoff arc, which is a failure transition, cannot be traversed, hence the destination of the n-gram arc s j will be the next state in p. in M , both the n-gram transition labeled with w and the backoff transition, now labeled with , can be traversed.


From Table 1(a) we see that Povey is faster and demands less memory compared with TC.

results using Povey with general determinization show that the memory demands between the two approaches are similar in the absence of the specialized determinization.


HALOGEN is thus domain-independent, and it was successfully ported to a specific dialogue system domain (Chambers and Allen 2004).

its performance depends largely on the granularity of the underlying meaning representation, which typically includes syntactic and lexical information.


More recent work has investigated other types of reranking models, such as hierarchical syntactic language models (Bangalore and Rambow 2000), discriminative models trained to replicate user ratings of utterance quality (Walker, Rambow, and Rogati 2002), or language models trained on speaker-specific corpora to model linguistic alignment (Isard, Brockmann, and Oberlander 2006).

a major drawback of the utterancelevel overgenerate and rank approach is its inherent computational cost.


This article is based on the assumption that learning to produce paraphrases can be facilitated by collecting data from a large sample of annotators.

this requires that the meaning representation should (a) be simple enough to be understood by untrained annotators, and (b) provide useful generalization properties for generating unseen inputs.


Such instances are therefore likely to occur simultaneously in the training and test partitions.

in our evaluation such templates are mapped to the same meaning representation, and we enforce the condition that the generated meaning representation was not seen during training.


2006), and suggested that users prefer dialogue systems in which repetitions are signaled (e.g., as I said before), even though that preference was not significant (Foster and White 2005).

we do not know of any research applying statistical paraphrasing techniques to dialogue.


BAGEL's granularity is defined by the semantic annotation in the training data, rather than external linguistic knowledge about what constitutes a unit of meaning; namely, contiguous words belonging to the same semantic stack are modeled as an atomic observation unit or phrase.

2 with word-level language models, a major advantage of phrase-based generation models is that they can model long-range dependencies and domain-specific idiomatic phrases with fewer parameters.


In terms of computational complexity, the number of stack sequences Order(S m ) to search over during the first decoding step increases exponentially with the number of input mandatory stacks.

the proposed three-stage architecture allows for tractable decoding by (a) pruning low probability paths during each Viterbi search, and (b) pruning low probability sequences from the output n-best list of each component.


Because a dialogue act can typically be conveyed in a large number of ways, it seems natural to model the NLG task as a one-to-many mapping.

previous work on statistical NLG has typically focused on evaluating the top ranked utterance, without evaluating whether the generator can produce paraphrases matching a reference paraphrase set (Langkilde-Geary 2002;Reiter and Belz 2009).


We first propose taking a sample from the top of the n-best list produced by BAGEL's realization reranking FLM shown in Table 2.

to avoid sampling from the long tail of low-probability utterances, we only consider utterances whose probability lies within a selection beam relative to the probability first-best utterance p 1 ; that is, only the utterances generated with a probability above are kept.


FLMs can be trained easily by estimating conditional probabilities from feature counts over a corpus, and they offer efficient decoding techniques for real-time generation.

fLMs do not scale well to large feature sets (i.e., contexts), as each additional feature increases the amount of data required to accurately estimate the fLM's conditional probability distribution.


The trigram model performs best without including any slot in the utterance class, with a mean BLEU score of .28.

bAGEL produces a score of .37 on the same data (using the most likely utterance only).


This limitation could be alleviated by including the n slots for which we want to control the lexical realization as part of the utterance class.

this is not tractable as it would require fragmenting the data further to produce all 2 n slot combinations as distinct utterance classes.


the handcrafted gold over the FLM reranker with n-best outputs, as no significance was reached over 600 comparisons (p < 0.05).

the judges preferred the handcrafted generator over the perceptron reranker, possibly because it was also perceived as significantly more natural (p < 0.05).


It is important to note that crowdsourced evaluations can lead to additional noise compared with standard lab-based evaluation, mostly due to the possibility of uncooperative evaluators.

the randomization of the order of the evaluated utterances ensures that such noise does not bias the results towards one system.


The model proposed by Yu and Joachims (2009) is highly related to ours because it is also based on latent trees.

they use undirected trees to represent clusters, whereas we use directed trees.


The CoNLL-2012 Shared Task data sets also include coreferring mentions of events.

the current version of our system does not consider verbs when creating candidate mentions and therefore does not resolve coreferences involving events.


In such a case, the correct prediction of y y y is impossible.

such cases are rare, given that the recall of the used sieves is approximately 90%.


At the same time, most structure learning algorithms are based on linear models, as such algorithms have strong theoretical guarantees regarding their prediction performance and, moreover, are computationally efficient.

linear models using basic features alone do not capture enough information to effectively represent coreference dependencies.


In this situation, the prediction of a completely correct clustering is impossible; that is, F y (F h (x x x, y y y)) = y y y for any model parameters.


's system, sieves are ordered from higher to lower precision.

in our filtering strategy, precision is not a concern, and the application order is not important.


From Table 3, it can be further observed that the best scores on Chinese and English are similar.

the performances on the Arabic language are much lower.


In the CoNLL-2012 data sets, for both English and Arabic, only the outer noun phrases are considered as mentions.

in the Chinese newswire documents, nested mentions are annotated as coreferring (Chen and Ng 2012).


The most frequent errors are pronouns, followed by proper nouns.

in the English language, the proportion of pronouns is much higher than the proportion of proper nouns, most likely due Table 17 Most frequent errors whenever an incorrect arc (i, j) is predicted instead of the correct arc (i * , j).


Cluster-sensitive features can further extend our modeling by considering features that are more strongly adherent to the coreference task.

as far as we know, there is as yet no structure learning system that considers such features.


Recently, Zhong and Ng (2009) tackled this problem by using a bilingual dictionary.

the dictionary has to be aligned to the sense inventory of interest (e.g., WordNet) and a large parallel corpus must be available that covers the full range of meanings in a lexicon.


The approach, implemented in a system based on Support Vector Machines and called It Makes Sense (Zhong and Ng 2010, IMS), attains state-of-the-art performance on lexical sample and all-words WSD tasks.

according to our calculation on the available models, 2 this approach can only provide training examples for about one third of ambiguous nouns in WordNet, more than half of which have only one of their senses covered.


Experimental results show that the joint use of multilingual knowledge enables further improvements over monolingual WSD.

the power of this disambiguation system lies mainly in its usage of the BabelNet multilingual semantic network.


We propose a new approach to the generation of pseudowords that enables the creation of semantically aware pseudowords while tackling the coverage and flexibility issues of the vicinity-based approach.

to the vicinity-based method, which takes as its search space the surroundings of a sense, our technique considers the WordNet semantic network in its entirety, hence enabling us to determine a graded degree of similarity between a given sense and all other synsets in WordNet.


the list similarSynsets to select a pseudosense.

the mode statistics in the table suggests that even when minFreq is set to a large value, most of the pseudosenses are picked out from the highest-ranking positions in the similarSynsets list.


In order to maximize the possibility of preserving the meaning of the original synset, a pseudosense should be selected from the set of words in the same synset, or in the directly related synsets (e.g., hypernym synsets).

many of the WordNet synsets do not contain monosemous terms and the similarity-based approach often needs to look further into the other indirectly related synsets so as to find a suitable pseudosense.


Ideally, the corpus used for calculating these statistics should be fully sense-tagged, namely, each usage of an ambiguous co-occurring word tagged with the intended sense.

because our training data (as is customary for WSD lexical sample data sets) do not provide sense annotations for context words, these edges are semi-noisy in that we connect an unambiguous endpoint w i to all senses of w .


across sense ranking, irrespective of the distribution of the training data.

iMS is not equally robust across configurations: Although its recall is relatively stable in the Uni-Nat configuration, it is not when the training set is naturally distributed (Nat-Nat).


We observed that IMS attains an optimal recall value of 100.0 for all data set sizes and for both sense distributions (i.e., uniform and natural distributions), showing that its models perfectly fit the training data.

as mentioned earlier in Section 6.5, in our setting the automatic enrichment of the LKB is less immediate and natural than the training of the supervised system.


In fact, as the number of training sentences increases, more reliable sets of related words get selected that are likely to provide semantic edges that are more beneficial.

the value of K, and hence the number of additional edges, remains almost constant across different sizes of the training data.


So, if the user model believes that the user cannot associate an attribute-value pair (e.g., < category, recliner >) to the target entity x, then it would return false.

if he can instead associate the pair (e.g., < category, chair >) to x, the user model would return true.


Therefore, using an accurate user model, an appropriate choice can be made to suit the user.

these models are static and are predefined before run-time.


In large domains, a large number of explicit sensing questions would need to be asked, which could be unwieldy.

we aim to sense each user's domain knowledge implicitly by using expert technical (or "jargon") expressions within the interaction.


Usually, in fully automated dialogue systems, automatic speech recognition (ASR) and natural language understanding (NLU) modules are used.

we use a human wizard to play the roles of ASR and NLU modules, so that we can focus on only the user modeling and NLG problem.


Recently, Lemon (2008), Rieser and Lemon (2009), and Dethlefs and Cuayahuitl (2010) have extended this approach to NLG to learn NLG policies to choose the appropriate attributes and strategies in information presentation tasks.

to our knowledge, the application of RL for dynamically modeling users' domain knowledge and generation of referring expressions based on user's domain knowledge is novel.